Improving resolution by image registration
CVGIP: Graphical Models and Image Processing
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics
Convex MRF potential functions
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Multi-sensor Satellite Image Sub-pixel Registration
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
MAP-MRF super-resolution image reconstruction using maximum pseudo-likelihood parameter estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Spatio-temporal resolution enhancement of vocal tract MRI sequences based on image registration
Integrated Computer-Aided Engineering
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High spatial resolution images are usually required in a great number of applications such as video surveillance, for instance. Super-Resolution reconstruction methods use image processing techniques to estimate a high-resolution image based on a set of low-resolution observations of the same scene. Therefore, these methods are able to overcome cost and hardware limitations inherent to acquisition devices. This paper discusses a Maximum a Posteriori Probability approach, characterizing the high-resolution estimation with the Isotropic Multi-Level Logistic Model that incorporates pixel similarity in a meaningful way to the super-resolution context. Following, the high-resolution estimation is derived by maximizing the local conditional probabilities sequentially with the Iterated Conditional Modes algorithm. The proposed method was evaluated in a simulated framework using the Normalized Mean Square Error criterion, and in a real situation using video frames. The results indicate the effectiveness of our approach both by numerical and visual evaluation.